Daniel Ferrante
Partner at SFL Scientific
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDaniel Ferrante is a founder and partner with 14+ years of experience building production-grade AI and data science solutions across private and public sectors, currently leading teams at Deloitte and SFL Scientific. He blends deep theoretical physics training (Ph.D. from Brown) with hands-on machine learning, high-performance computing, and applied mathematical modeling to tackle large-scale problems from neuroscience (multi-petabyte brain imaging analytics) to pharma and energy. A recognized industry partner (multiple NVIDIA Partner of the Year awards, AWS Advanced ML and Azure AI partnerships), he specializes in taking advanced algorithms from research to operational systems. His background in non-perturbative quantum field theory and novel numerical methods informs unconventional approaches—such as topology-based analyses and Imaginary Monte Carlo—to real-world data challenges. Comfortable with infrastructure through to leadership, he has managed HPC commissioning, security, and DevOps while scaling boutique consulting engagements into enterprise-grade deliveries. Based in New York, he is known for bridging deep math/physics insights with pragmatic productization of AI.
14 years of coding experience
11 years of employment as a software developer
Ph.D. Physics, Ph.D. Physics at Brown UniversityB.Sc. Physics Mathematics Computer Science Chemistry Biology, B.Sc. Physics Mathematics Computer Science Chemistry Biology at USP - Universidade de São Paulo
Portuguese, English, Spanish, Italian, French
Github Skills (5)
ruby9
linux8
homebrew8
macos7
package-manager7
Programming languages (5)
Github contributions (5)
danieldf/danieldf.github.com
Mar 2012 - Aug 2014
Contributions:57 commits, 1 push in 2 years 4 months
danieldf/danieldf.github.io
Jan 2019 - Jan 2019
Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes
Contributions:41 pushes in 1 day
templategithub-pages-templatemmistakesmistakesjekyll
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